The development, status and trends of recommender systems: a comprehensive and critical literature review

Recommender systems have been used in many fields of research and business applications. In this paper, a comprehensive and critical review of the literature on recommender systems is provided. A classification mechanism of recommender systems is proposed. The review pays attention to and covers the recommender system algorithms, application areas and data mining techniques published in relevant peer-reviewed journals between 2001 and 2013. The development of the field, status and trends are analyzed and discussed in the paper

A New Approach for Trust Prediction by using collaborative filtering based of Pareto dominance in Social Networks

Along with the increasing popularity of social web sites, users rely more on the trustworthiness informationfor many online activities among users.[24] However, such social network data often suffers from two problems,(1)severe data sparsity and are not able to provide users with enough information, (2)dataset’s is very large.Therefore, trust prediction has emerged as an important topic in social network research. In this paper weproposed a new approach by using collaborative filtering method and the concept of Pareto dominance. We usesPareto dominance to perform a pre-filtering process eliminating less representative users from the k-neighbourselection process while retaining the most promising ones. The results from experiments performed on FilmTrustdataset and Epinions dataset

From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks

Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks in terms of risk management. The proposed method has been experimentally evaluated and validated, with the results showing that it is both practical and effective